Free Using HPE AI and Machine Learnin Exam HPE2-N69 Exam Practice Test

What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

• it downloads datasets for training.
• It uploads model checkpoints.
• It validates trained models.
• It ensures experiment metadata is stored.

You are meeting with a customer, and MUDL engineers express frustration about losing work flue to hardware failures. What should you explain about how HPE Machine Learning Development Environment addresses this pain point?

• The solution automatically mirrors the training process on redundant agents, which take over If an issue occurs.
• The solution continuously monitors agent hardware and sends out proactive alerts before failed hardware causes training to tail.
• The conductor and each of the agents ate deployed in an active-standby model, which protects in case of hardware issues.
• The solution can take periodic checkpoints during the training process and automatically restart failed training from the latest checkpoint.

What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

• Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
• Slingshot
• InfiniBand
• Data Center Bridging (OCB)-enabled Ethernet

A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?

• Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
• Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
• Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
• Establishing multiple compute resource pools on the cluster, one tor servers or each type

A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?

• Set the weight to a higher than default value.
• Set the weight to a lower than default value.
• Set the priority to a lower than default value.
• Set the priority to a higher than default value.

What are the mechanics of now a model trains?

• Decides which algorithm can best meet the use case for the application in question
• Adjusts the model's parameter weights such that the model can Better perform its tasks
• Tests how accurately the model performs on a wide array of real world data
• Detects Data drift of content drift that might compromise the ML model's performance

An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50* Experiment 3; l trial (Trial 3) that needs 24 slots; priority IWhat happens?

• Trial I is allowed to finish. Then Trial 3 is scheduled.
• Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
• Trial 1 is allowed to finish. Then Trial 2 is scheduled.
• Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?

• Automated user provisioning
• Pipeline-based data management
• Distributed training
• Automated hyperparameter optimization (HPO)

What is one key target vertical (or HPE Machine Learning Development solutions?

• Hospitality
• K-12education
• Retail
• Manufacturing

You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use 'det deploy' to set up the cluster. What is one prerequisite?

• installing the NVIDIA Container Toolkit on your local machine
• Manually creating the AWS EC2 instance with a PostgreSQL database
• Recording the name of a valid AWS EC2 keypair
• Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account